Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

# Use this For lower tensor flow 
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

#use this For higher tensor flow 
#mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'RGB')
#pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[2]:
<matplotlib.image.AxesImage at 0x21e53343b00>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
celeb_mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(celeb_mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x21e53403550>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None,image_height,image_width, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None,z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32,name='learning_rate')  
#    lr = tf.placeholder(float,shape=(),name='learning_rate')  
#    learning_rate =0.002

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha=0.1
    keep_prob= 0.9
    # TODO: Implement Function
    
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input is 28x28x3
        
        # make size 14x14x64
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2,  padding='same', activation=None)
        lrelu1 = tf.maximum(conv1 * alpha, conv1)
        
        # change to 7x7x128
        conv2 = tf.layers.conv2d(lrelu1, 128, 5, strides=2, padding='same', activation=None)
        bn1 = tf.layers.batch_normalization(conv2, training=True)
        lrelu2 = tf.maximum(bn1 * alpha, bn1)
#        drp1 = tf.nn.dropout(lrelu2, keep_prob = keep_prob)
        
        
        # 4x4x256
        conv3 = tf.layers.conv2d(lrelu2, 256, 5, strides=2,  padding='same')
#        conv3 = tf.layers.conv2d(drp1, 256, 5, strides=2,  padding='same')
        bn2 = tf.layers.batch_normalization(conv3, training=True)
        lrelu3 = tf.maximum(bn2 * alpha, bn2)
#        drp2 = tf.nn.dropout(lrelu3, keep_prob = keep_prob)
        
        
        flat = tf.reshape(lrelu3, (-1, 4*4*256))
#        flat = tf.reshape(drp2, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.1
    keep_prob = 0.9
    
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        # shape = 7x7x512
        x1 = tf.layers.dense(z, 7 * 7 * 512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        # shape = 14x14x256
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
#        x2 = tf.nn.dropout(x2, keep_prob=keep_prob)
        
        # shape = 28x28x128
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
#        x3 = tf.nn.dropout(x3, keep_prob=keep_prob)
        
        # Output layer
        # shape = 28x28x5
#        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 3, strides=1, padding='same')
        
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    alpha = 0.1
    keep_prob = 0.9
    smooth = 0.1
    #discriminator(images, reuse=False)
    #generator(z, out_channel_dim, is_train=True)
    # Here we run the generator and the discriminator
    # Build the Model, g_model is the generator output
    g_model = generator(input_z, out_channel_dim,is_train=True)
    
    d_model_real, d_logits_real = discriminator(input_real,reuse=False)
    d_model_fake, d_logits_fake  = discriminator(g_model, reuse=True)
    
    
    
    
    # Here we compute `d_loss`, the loss for the discriminator.
    # This should combine two different losses:
    #  1. The loss for the GAN problem, where we minimize the cross-entropy for the binary
    #     real-vs-fake classification problem.
    #  2. The loss for the SVHN digit classification problem, where we minimize the cross-entropy
    #     for the multi-class softmax. For this one we use the labels. Don't forget to ignore
    #     use `label_mask` to ignore the examples that we are pretending are unlabeled for the
    #     semi-supervised learning problem.
    d_loss_real = tf.reduce_mean(
                  tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                          labels=tf.ones_like(d_logits_real) * (1 - smooth)))
#    d_loss_fake = tf.reduce_mean(
#                  tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
#                                                          labels=tf.zeros_like(d_logits_real)))
    d_loss_fake = tf.reduce_mean(
                  tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                          labels=tf.zeros_like(d_logits_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    
    g_loss = tf.reduce_mean(
             tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                     labels=tf.ones_like(d_logits_fake)))
    
  
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    #learning_rate = 0.002

    # Get the trainable_variables, split into G and D parts
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    #d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)
    #g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)
    
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

GPU Constraint

https://www.tensorflow.org/programmers_guide/using_gpu

config = tf.ConfigProto() config.gpu_options.allow_growth = True session = tf.Session(config=config, ...)

config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.4 session = tf.Session(config=config, ...)

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    n_samples,image_width, image_height, image_channels = data_shape
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1) 
    
    #saver = tf.train.Saver(var_list=g_vars)
    saver = tf.train.Saver()
    samples, losses = [], []
    steps = 0
    print_every=10
    show_every=100
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
#    config.gpu_options.per_process_gpu_memory_fraction = 0.4
#    with tf.Session() as sess:
    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizersbatch_images, lr: learning_rate 
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                #_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
#  _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate: learning_rate })
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images,input_z: batch_z, lr:learning_rate })
                
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Step :",steps,"Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                    
                if steps % show_every == 0:
                    n_images = 16
                    show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)
                    
        saver.save(sess, './checkpoint/g_chkpoint.ckpt')
    return losses

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [25]:
batch_size = 28
z_dim = 50
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
#mnist_images_2 = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'RGB')
#pyplot.imshow(helper.images_square_grid(mnist_images_2[:25], 'RGB'))

print(mnist_dataset.shape,mnist_dataset.image_mode )
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
    
print("MNIST Complete")    
(60000, 28, 28, 1) L
Step : 10 Epoch 1/2... Discriminator Loss: 0.7736... Generator Loss: 1.2389
Step : 20 Epoch 1/2... Discriminator Loss: 0.7539... Generator Loss: 1.2448
Step : 30 Epoch 1/2... Discriminator Loss: 0.6070... Generator Loss: 1.6147
Step : 40 Epoch 1/2... Discriminator Loss: 0.3979... Generator Loss: 2.9421
Step : 50 Epoch 1/2... Discriminator Loss: 0.3734... Generator Loss: 3.5174
Step : 60 Epoch 1/2... Discriminator Loss: 0.3559... Generator Loss: 4.0647
Step : 70 Epoch 1/2... Discriminator Loss: 1.6038... Generator Loss: 0.5083
Step : 80 Epoch 1/2... Discriminator Loss: 2.2892... Generator Loss: 0.2600
Step : 90 Epoch 1/2... Discriminator Loss: 1.2445... Generator Loss: 0.9897
Step : 100 Epoch 1/2... Discriminator Loss: 1.2294... Generator Loss: 0.9883
Step : 110 Epoch 1/2... Discriminator Loss: 1.2348... Generator Loss: 1.1027
Step : 120 Epoch 1/2... Discriminator Loss: 1.3229... Generator Loss: 0.7006
Step : 130 Epoch 1/2... Discriminator Loss: 1.2927... Generator Loss: 0.8699
Step : 140 Epoch 1/2... Discriminator Loss: 1.2127... Generator Loss: 0.6593
Step : 150 Epoch 1/2... Discriminator Loss: 1.1256... Generator Loss: 0.8147
Step : 160 Epoch 1/2... Discriminator Loss: 1.2490... Generator Loss: 0.8916
Step : 170 Epoch 1/2... Discriminator Loss: 1.1817... Generator Loss: 1.2486
Step : 180 Epoch 1/2... Discriminator Loss: 1.1430... Generator Loss: 1.2658
Step : 190 Epoch 1/2... Discriminator Loss: 1.0196... Generator Loss: 1.1411
Step : 200 Epoch 1/2... Discriminator Loss: 1.0695... Generator Loss: 0.9195
Step : 210 Epoch 1/2... Discriminator Loss: 1.1624... Generator Loss: 0.9798
Step : 220 Epoch 1/2... Discriminator Loss: 1.1304... Generator Loss: 1.3025
Step : 230 Epoch 1/2... Discriminator Loss: 0.8955... Generator Loss: 1.2071
Step : 240 Epoch 1/2... Discriminator Loss: 0.9783... Generator Loss: 1.1664
Step : 250 Epoch 1/2... Discriminator Loss: 1.2170... Generator Loss: 0.6511
Step : 260 Epoch 1/2... Discriminator Loss: 0.9619... Generator Loss: 1.4252
Step : 270 Epoch 1/2... Discriminator Loss: 0.9736... Generator Loss: 1.9476
Step : 280 Epoch 1/2... Discriminator Loss: 0.9653... Generator Loss: 1.1803
Step : 290 Epoch 1/2... Discriminator Loss: 0.8071... Generator Loss: 1.3742
Step : 300 Epoch 1/2... Discriminator Loss: 0.8654... Generator Loss: 1.4037
Step : 310 Epoch 1/2... Discriminator Loss: 0.8914... Generator Loss: 1.1606
Step : 320 Epoch 1/2... Discriminator Loss: 0.9696... Generator Loss: 0.9436
Step : 330 Epoch 1/2... Discriminator Loss: 0.7707... Generator Loss: 1.3042
Step : 340 Epoch 1/2... Discriminator Loss: 0.9506... Generator Loss: 0.9879
Step : 350 Epoch 1/2... Discriminator Loss: 0.9791... Generator Loss: 1.1082
Step : 360 Epoch 1/2... Discriminator Loss: 0.7598... Generator Loss: 1.5576
Step : 370 Epoch 1/2... Discriminator Loss: 0.8365... Generator Loss: 1.3794
Step : 380 Epoch 1/2... Discriminator Loss: 0.7785... Generator Loss: 1.5206
Step : 390 Epoch 1/2... Discriminator Loss: 0.6727... Generator Loss: 1.7786
Step : 400 Epoch 1/2... Discriminator Loss: 0.6265... Generator Loss: 2.0578
Step : 410 Epoch 1/2... Discriminator Loss: 0.9986... Generator Loss: 0.9656
Step : 420 Epoch 1/2... Discriminator Loss: 0.7942... Generator Loss: 1.4291
Step : 430 Epoch 1/2... Discriminator Loss: 0.7138... Generator Loss: 1.5655
Step : 440 Epoch 1/2... Discriminator Loss: 0.6585... Generator Loss: 1.6392
Step : 450 Epoch 1/2... Discriminator Loss: 0.8596... Generator Loss: 1.1674
Step : 460 Epoch 1/2... Discriminator Loss: 0.9548... Generator Loss: 0.9707
Step : 470 Epoch 1/2... Discriminator Loss: 0.9182... Generator Loss: 1.0112
Step : 480 Epoch 1/2... Discriminator Loss: 0.7667... Generator Loss: 1.4466
Step : 490 Epoch 1/2... Discriminator Loss: 1.0120... Generator Loss: 0.9587
Step : 500 Epoch 1/2... Discriminator Loss: 0.9514... Generator Loss: 0.9876
Step : 510 Epoch 1/2... Discriminator Loss: 0.8499... Generator Loss: 2.0453
Step : 520 Epoch 1/2... Discriminator Loss: 1.2641... Generator Loss: 0.5815
Step : 530 Epoch 1/2... Discriminator Loss: 0.7920... Generator Loss: 1.4003
Step : 540 Epoch 1/2... Discriminator Loss: 0.9176... Generator Loss: 1.0696
Step : 550 Epoch 1/2... Discriminator Loss: 1.3582... Generator Loss: 0.5782
Step : 560 Epoch 1/2... Discriminator Loss: 1.3697... Generator Loss: 0.5832
Step : 570 Epoch 1/2... Discriminator Loss: 0.7411... Generator Loss: 1.7453
Step : 580 Epoch 1/2... Discriminator Loss: 0.9713... Generator Loss: 0.9608
Step : 590 Epoch 1/2... Discriminator Loss: 1.0167... Generator Loss: 1.6099
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Step : 3310 Epoch 2/2... Discriminator Loss: 1.2145... Generator Loss: 0.7486
Step : 3320 Epoch 2/2... Discriminator Loss: 0.9517... Generator Loss: 1.0956
Step : 3330 Epoch 2/2... Discriminator Loss: 0.7646... Generator Loss: 1.9475
Step : 3340 Epoch 2/2... Discriminator Loss: 0.8336... Generator Loss: 1.3190
Step : 3350 Epoch 2/2... Discriminator Loss: 1.1263... Generator Loss: 0.7543
Step : 3360 Epoch 2/2... Discriminator Loss: 1.1010... Generator Loss: 0.7177
Step : 3370 Epoch 2/2... Discriminator Loss: 0.7600... Generator Loss: 1.5159
Step : 3380 Epoch 2/2... Discriminator Loss: 0.9146... Generator Loss: 1.1387
Step : 3390 Epoch 2/2... Discriminator Loss: 0.8500... Generator Loss: 1.2450
Step : 3400 Epoch 2/2... Discriminator Loss: 1.0440... Generator Loss: 1.0696
Step : 3410 Epoch 2/2... Discriminator Loss: 1.0992... Generator Loss: 1.4548
Step : 3420 Epoch 2/2... Discriminator Loss: 0.8600... Generator Loss: 1.2244
Step : 3430 Epoch 2/2... Discriminator Loss: 1.4485... Generator Loss: 0.5650
Step : 3440 Epoch 2/2... Discriminator Loss: 1.2155... Generator Loss: 0.6264
Step : 3450 Epoch 2/2... Discriminator Loss: 1.1356... Generator Loss: 0.8968
Step : 3460 Epoch 2/2... Discriminator Loss: 0.8937... Generator Loss: 1.1229
Step : 3470 Epoch 2/2... Discriminator Loss: 1.0848... Generator Loss: 0.7811
Step : 3480 Epoch 2/2... Discriminator Loss: 0.7536... Generator Loss: 1.2835
Step : 3490 Epoch 2/2... Discriminator Loss: 1.0182... Generator Loss: 0.8080
Step : 3500 Epoch 2/2... Discriminator Loss: 1.1577... Generator Loss: 0.7278
Step : 3510 Epoch 2/2... Discriminator Loss: 0.9917... Generator Loss: 0.9338
Step : 3520 Epoch 2/2... Discriminator Loss: 1.0270... Generator Loss: 0.8204
Step : 3530 Epoch 2/2... Discriminator Loss: 1.1939... Generator Loss: 0.7250
Step : 3540 Epoch 2/2... Discriminator Loss: 0.6526... Generator Loss: 1.5793
Step : 3550 Epoch 2/2... Discriminator Loss: 1.0584... Generator Loss: 0.8863
Step : 3560 Epoch 2/2... Discriminator Loss: 0.7935... Generator Loss: 1.5376
Step : 3570 Epoch 2/2... Discriminator Loss: 1.0606... Generator Loss: 1.3697
Step : 3580 Epoch 2/2... Discriminator Loss: 0.8734... Generator Loss: 1.4303
Step : 3590 Epoch 2/2... Discriminator Loss: 0.9574... Generator Loss: 1.5200
Step : 3600 Epoch 2/2... Discriminator Loss: 1.1069... Generator Loss: 0.8017
Step : 3610 Epoch 2/2... Discriminator Loss: 0.7912... Generator Loss: 1.1073
Step : 3620 Epoch 2/2... Discriminator Loss: 1.3354... Generator Loss: 0.5709
Step : 3630 Epoch 2/2... Discriminator Loss: 1.1878... Generator Loss: 1.9473
Step : 3640 Epoch 2/2... Discriminator Loss: 0.9541... Generator Loss: 0.9457
Step : 3650 Epoch 2/2... Discriminator Loss: 0.8942... Generator Loss: 1.7830
Step : 3660 Epoch 2/2... Discriminator Loss: 0.9603... Generator Loss: 1.3211
Step : 3670 Epoch 2/2... Discriminator Loss: 0.8863... Generator Loss: 1.2954
Step : 3680 Epoch 2/2... Discriminator Loss: 1.0113... Generator Loss: 1.0891
Step : 3690 Epoch 2/2... Discriminator Loss: 1.1825... Generator Loss: 0.6500
Step : 3700 Epoch 2/2... Discriminator Loss: 0.8798... Generator Loss: 1.2666
Step : 3710 Epoch 2/2... Discriminator Loss: 0.8536... Generator Loss: 1.1521
Step : 3720 Epoch 2/2... Discriminator Loss: 0.8334... Generator Loss: 1.2341
Step : 3730 Epoch 2/2... Discriminator Loss: 0.8183... Generator Loss: 1.1553
Step : 3740 Epoch 2/2... Discriminator Loss: 0.8009... Generator Loss: 1.3052
Step : 3750 Epoch 2/2... Discriminator Loss: 0.8432... Generator Loss: 1.1967
Step : 3760 Epoch 2/2... Discriminator Loss: 1.1277... Generator Loss: 0.8050
Step : 3770 Epoch 2/2... Discriminator Loss: 0.8907... Generator Loss: 1.0639
Step : 3780 Epoch 2/2... Discriminator Loss: 0.9258... Generator Loss: 1.1220
Step : 3790 Epoch 2/2... Discriminator Loss: 0.7928... Generator Loss: 1.5963
Step : 3800 Epoch 2/2... Discriminator Loss: 1.2301... Generator Loss: 0.6408
Step : 3810 Epoch 2/2... Discriminator Loss: 0.8645... Generator Loss: 1.2186
Step : 3820 Epoch 2/2... Discriminator Loss: 0.9790... Generator Loss: 1.1265
Step : 3830 Epoch 2/2... Discriminator Loss: 1.0326... Generator Loss: 0.8652
Step : 3840 Epoch 2/2... Discriminator Loss: 0.8147... Generator Loss: 1.2267
Step : 3850 Epoch 2/2... Discriminator Loss: 1.2312... Generator Loss: 0.6330
Step : 3860 Epoch 2/2... Discriminator Loss: 0.8393... Generator Loss: 1.9373
Step : 3870 Epoch 2/2... Discriminator Loss: 0.8125... Generator Loss: 1.8259
Step : 3880 Epoch 2/2... Discriminator Loss: 0.7204... Generator Loss: 1.3686
Step : 3890 Epoch 2/2... Discriminator Loss: 1.0263... Generator Loss: 1.8682
Step : 3900 Epoch 2/2... Discriminator Loss: 0.7679... Generator Loss: 1.4277
Step : 3910 Epoch 2/2... Discriminator Loss: 0.7923... Generator Loss: 1.3142
Step : 3920 Epoch 2/2... Discriminator Loss: 0.7732... Generator Loss: 1.2238
Step : 3930 Epoch 2/2... Discriminator Loss: 0.9230... Generator Loss: 1.0873
Step : 3940 Epoch 2/2... Discriminator Loss: 0.8455... Generator Loss: 1.4904
Step : 3950 Epoch 2/2... Discriminator Loss: 1.3018... Generator Loss: 0.5818
Step : 3960 Epoch 2/2... Discriminator Loss: 0.8074... Generator Loss: 1.4467
Step : 3970 Epoch 2/2... Discriminator Loss: 1.0516... Generator Loss: 0.8626
Step : 3980 Epoch 2/2... Discriminator Loss: 1.0425... Generator Loss: 0.8564
Step : 3990 Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 1.2795
Step : 4000 Epoch 2/2... Discriminator Loss: 0.8884... Generator Loss: 1.1825
Step : 4010 Epoch 2/2... Discriminator Loss: 1.1092... Generator Loss: 0.7676
Step : 4020 Epoch 2/2... Discriminator Loss: 0.9295... Generator Loss: 0.9595
Step : 4030 Epoch 2/2... Discriminator Loss: 0.8434... Generator Loss: 1.3066
Step : 4040 Epoch 2/2... Discriminator Loss: 1.0125... Generator Loss: 1.5728
Step : 4050 Epoch 2/2... Discriminator Loss: 1.2365... Generator Loss: 0.6191
Step : 4060 Epoch 2/2... Discriminator Loss: 0.8585... Generator Loss: 1.0872
Step : 4070 Epoch 2/2... Discriminator Loss: 0.7370... Generator Loss: 2.0450
Step : 4080 Epoch 2/2... Discriminator Loss: 1.8095... Generator Loss: 0.3265
Step : 4090 Epoch 2/2... Discriminator Loss: 0.8147... Generator Loss: 1.1321
Step : 4100 Epoch 2/2... Discriminator Loss: 0.7395... Generator Loss: 1.5172
Step : 4110 Epoch 2/2... Discriminator Loss: 0.4455... Generator Loss: 2.5947
Step : 4120 Epoch 2/2... Discriminator Loss: 1.1402... Generator Loss: 0.8877
Step : 4130 Epoch 2/2... Discriminator Loss: 1.3923... Generator Loss: 0.4885
Step : 4140 Epoch 2/2... Discriminator Loss: 0.9149... Generator Loss: 1.4092
Step : 4150 Epoch 2/2... Discriminator Loss: 1.1134... Generator Loss: 0.7491
Step : 4160 Epoch 2/2... Discriminator Loss: 0.9332... Generator Loss: 1.4155
Step : 4170 Epoch 2/2... Discriminator Loss: 0.6553... Generator Loss: 1.6258
Step : 4180 Epoch 2/2... Discriminator Loss: 0.8347... Generator Loss: 1.3329
Step : 4190 Epoch 2/2... Discriminator Loss: 0.8088... Generator Loss: 1.3373
Step : 4200 Epoch 2/2... Discriminator Loss: 1.0505... Generator Loss: 0.7900
Step : 4210 Epoch 2/2... Discriminator Loss: 0.9069... Generator Loss: 0.9560
Step : 4220 Epoch 2/2... Discriminator Loss: 1.1222... Generator Loss: 0.7626
Step : 4230 Epoch 2/2... Discriminator Loss: 0.8066... Generator Loss: 1.2643
Step : 4240 Epoch 2/2... Discriminator Loss: 0.8462... Generator Loss: 1.1347
Step : 4250 Epoch 2/2... Discriminator Loss: 0.7697... Generator Loss: 1.3393
Step : 4260 Epoch 2/2... Discriminator Loss: 0.8769... Generator Loss: 1.2992
Step : 4270 Epoch 2/2... Discriminator Loss: 0.7444... Generator Loss: 1.2878
Step : 4280 Epoch 2/2... Discriminator Loss: 1.0617... Generator Loss: 0.7488
MNIST Complete

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [26]:
#batch_size = 64
#z_dim = 100
#learning_rate = 0.0002
#beta1 = 0.5

batch_size = 28
z_dim = 100
learning_rate = 0.0004
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
print("celeba complete")
Step : 10 Epoch 1/1... Discriminator Loss: 0.4334... Generator Loss: 3.1722
Step : 20 Epoch 1/1... Discriminator Loss: 0.5450... Generator Loss: 11.8285
Step : 30 Epoch 1/1... Discriminator Loss: 0.3962... Generator Loss: 3.1544
Step : 40 Epoch 1/1... Discriminator Loss: 0.4335... Generator Loss: 6.8734
Step : 50 Epoch 1/1... Discriminator Loss: 1.3839... Generator Loss: 0.9763
Step : 60 Epoch 1/1... Discriminator Loss: 1.3531... Generator Loss: 3.3534
Step : 70 Epoch 1/1... Discriminator Loss: 0.9413... Generator Loss: 4.1396
Step : 80 Epoch 1/1... Discriminator Loss: 0.7652... Generator Loss: 1.5799
Step : 90 Epoch 1/1... Discriminator Loss: 0.5708... Generator Loss: 3.1448
Step : 100 Epoch 1/1... Discriminator Loss: 0.4431... Generator Loss: 3.1399
Step : 110 Epoch 1/1... Discriminator Loss: 0.5139... Generator Loss: 2.2756
Step : 120 Epoch 1/1... Discriminator Loss: 0.6259... Generator Loss: 3.2839
Step : 130 Epoch 1/1... Discriminator Loss: 0.7255... Generator Loss: 2.8293
Step : 140 Epoch 1/1... Discriminator Loss: 0.6621... Generator Loss: 3.9484
Step : 150 Epoch 1/1... Discriminator Loss: 1.2371... Generator Loss: 0.8934
Step : 160 Epoch 1/1... Discriminator Loss: 0.6421... Generator Loss: 1.9695
Step : 170 Epoch 1/1... Discriminator Loss: 1.6907... Generator Loss: 4.7028
Step : 180 Epoch 1/1... Discriminator Loss: 1.4463... Generator Loss: 0.7313
Step : 190 Epoch 1/1... Discriminator Loss: 1.0668... Generator Loss: 0.9062
Step : 200 Epoch 1/1... Discriminator Loss: 1.2434... Generator Loss: 0.7705
Step : 210 Epoch 1/1... Discriminator Loss: 1.5100... Generator Loss: 3.1480
Step : 220 Epoch 1/1... Discriminator Loss: 1.6925... Generator Loss: 4.0835
Step : 230 Epoch 1/1... Discriminator Loss: 1.4237... Generator Loss: 0.5663
Step : 240 Epoch 1/1... Discriminator Loss: 1.2081... Generator Loss: 1.9639
Step : 250 Epoch 1/1... Discriminator Loss: 1.3305... Generator Loss: 0.9354
Step : 260 Epoch 1/1... Discriminator Loss: 1.0713... Generator Loss: 0.9420
Step : 270 Epoch 1/1... Discriminator Loss: 0.9358... Generator Loss: 1.7335
Step : 280 Epoch 1/1... Discriminator Loss: 1.4108... Generator Loss: 0.5047
Step : 290 Epoch 1/1... Discriminator Loss: 1.3314... Generator Loss: 0.6279
Step : 300 Epoch 1/1... Discriminator Loss: 1.3586... Generator Loss: 0.7481
Step : 310 Epoch 1/1... Discriminator Loss: 0.8224... Generator Loss: 2.3140
Step : 320 Epoch 1/1... Discriminator Loss: 1.1828... Generator Loss: 0.7449
Step : 330 Epoch 1/1... Discriminator Loss: 2.6452... Generator Loss: 0.1294
Step : 340 Epoch 1/1... Discriminator Loss: 0.9805... Generator Loss: 1.2073
Step : 350 Epoch 1/1... Discriminator Loss: 0.8046... Generator Loss: 2.4153
Step : 360 Epoch 1/1... Discriminator Loss: 1.2265... Generator Loss: 0.9425
Step : 370 Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.8481
Step : 380 Epoch 1/1... Discriminator Loss: 1.0753... Generator Loss: 0.8857
Step : 390 Epoch 1/1... Discriminator Loss: 1.2657... Generator Loss: 1.2175
Step : 400 Epoch 1/1... Discriminator Loss: 1.6612... Generator Loss: 0.5460
Step : 410 Epoch 1/1... Discriminator Loss: 1.3742... Generator Loss: 0.8404
Step : 420 Epoch 1/1... Discriminator Loss: 1.5771... Generator Loss: 0.6861
Step : 430 Epoch 1/1... Discriminator Loss: 1.1797... Generator Loss: 1.0864
Step : 440 Epoch 1/1... Discriminator Loss: 1.0838... Generator Loss: 1.5861
Step : 450 Epoch 1/1... Discriminator Loss: 1.2036... Generator Loss: 2.9665
Step : 460 Epoch 1/1... Discriminator Loss: 1.4024... Generator Loss: 0.8147
Step : 470 Epoch 1/1... Discriminator Loss: 1.7272... Generator Loss: 0.3406
Step : 480 Epoch 1/1... Discriminator Loss: 0.8029... Generator Loss: 1.2399
Step : 490 Epoch 1/1... Discriminator Loss: 1.3743... Generator Loss: 0.7938
Step : 500 Epoch 1/1... Discriminator Loss: 1.2110... Generator Loss: 0.8072
Step : 510 Epoch 1/1... Discriminator Loss: 1.2539... Generator Loss: 1.3265
Step : 520 Epoch 1/1... Discriminator Loss: 1.1661... Generator Loss: 0.7229
Step : 530 Epoch 1/1... Discriminator Loss: 1.8798... Generator Loss: 2.3453
Step : 540 Epoch 1/1... Discriminator Loss: 0.6017... Generator Loss: 2.2240
Step : 550 Epoch 1/1... Discriminator Loss: 1.3805... Generator Loss: 0.4968
Step : 560 Epoch 1/1... Discriminator Loss: 1.2841... Generator Loss: 0.9752
Step : 570 Epoch 1/1... Discriminator Loss: 1.8214... Generator Loss: 2.1060
Step : 580 Epoch 1/1... Discriminator Loss: 1.0349... Generator Loss: 1.3810
Step : 590 Epoch 1/1... Discriminator Loss: 1.5126... Generator Loss: 0.4884
Step : 600 Epoch 1/1... Discriminator Loss: 1.3080... Generator Loss: 0.7371
Step : 610 Epoch 1/1... Discriminator Loss: 1.5318... Generator Loss: 1.0679
Step : 620 Epoch 1/1... Discriminator Loss: 0.9680... Generator Loss: 1.3772
Step : 630 Epoch 1/1... Discriminator Loss: 1.1209... Generator Loss: 0.9950
Step : 640 Epoch 1/1... Discriminator Loss: 1.5918... Generator Loss: 0.4154
Step : 650 Epoch 1/1... Discriminator Loss: 1.1535... Generator Loss: 0.7954
Step : 660 Epoch 1/1... Discriminator Loss: 1.4530... Generator Loss: 0.8554
Step : 670 Epoch 1/1... Discriminator Loss: 1.0297... Generator Loss: 1.1888
Step : 680 Epoch 1/1... Discriminator Loss: 1.2012... Generator Loss: 0.9508
Step : 690 Epoch 1/1... Discriminator Loss: 1.4202... Generator Loss: 0.4621
Step : 700 Epoch 1/1... Discriminator Loss: 1.8259... Generator Loss: 2.1987
Step : 710 Epoch 1/1... Discriminator Loss: 1.4188... Generator Loss: 0.7518
Step : 720 Epoch 1/1... Discriminator Loss: 1.3009... Generator Loss: 2.0385
Step : 730 Epoch 1/1... Discriminator Loss: 0.9785... Generator Loss: 0.9969
Step : 740 Epoch 1/1... Discriminator Loss: 1.4316... Generator Loss: 0.6419
Step : 750 Epoch 1/1... Discriminator Loss: 1.1735... Generator Loss: 0.9768
Step : 760 Epoch 1/1... Discriminator Loss: 1.2522... Generator Loss: 0.7779
Step : 770 Epoch 1/1... Discriminator Loss: 1.1724... Generator Loss: 0.7616
Step : 780 Epoch 1/1... Discriminator Loss: 1.3862... Generator Loss: 0.7027
Step : 790 Epoch 1/1... Discriminator Loss: 0.9784... Generator Loss: 1.2291
Step : 800 Epoch 1/1... Discriminator Loss: 1.5258... Generator Loss: 0.9165
Step : 810 Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.4399
Step : 820 Epoch 1/1... Discriminator Loss: 1.1694... Generator Loss: 0.8092
Step : 830 Epoch 1/1... Discriminator Loss: 1.7317... Generator Loss: 0.3307
Step : 840 Epoch 1/1... Discriminator Loss: 1.3084... Generator Loss: 1.4597
Step : 850 Epoch 1/1... Discriminator Loss: 1.4799... Generator Loss: 1.0088
Step : 860 Epoch 1/1... Discriminator Loss: 1.1960... Generator Loss: 0.8688
Step : 870 Epoch 1/1... Discriminator Loss: 1.3348... Generator Loss: 0.9212
Step : 880 Epoch 1/1... Discriminator Loss: 1.1459... Generator Loss: 0.9426
Step : 890 Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 0.4912
Step : 900 Epoch 1/1... Discriminator Loss: 1.7837... Generator Loss: 0.3236
Step : 910 Epoch 1/1... Discriminator Loss: 1.4171... Generator Loss: 0.4766
Step : 920 Epoch 1/1... Discriminator Loss: 1.4343... Generator Loss: 0.6248
Step : 930 Epoch 1/1... Discriminator Loss: 1.1461... Generator Loss: 1.1455
Step : 940 Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 0.5850
Step : 950 Epoch 1/1... Discriminator Loss: 1.2085... Generator Loss: 0.7813
Step : 960 Epoch 1/1... Discriminator Loss: 0.9729... Generator Loss: 1.3769
Step : 970 Epoch 1/1... Discriminator Loss: 1.5730... Generator Loss: 0.4453
Step : 980 Epoch 1/1... Discriminator Loss: 1.5134... Generator Loss: 1.6760
Step : 990 Epoch 1/1... Discriminator Loss: 1.4239... Generator Loss: 1.9512
Step : 1000 Epoch 1/1... Discriminator Loss: 0.6398... Generator Loss: 1.7513
Step : 1010 Epoch 1/1... Discriminator Loss: 1.3321... Generator Loss: 0.7527
Step : 1020 Epoch 1/1... Discriminator Loss: 1.1750... Generator Loss: 1.0123
Step : 1030 Epoch 1/1... Discriminator Loss: 2.4302... Generator Loss: 0.1558
Step : 1040 Epoch 1/1... Discriminator Loss: 1.2756... Generator Loss: 0.7705
Step : 1050 Epoch 1/1... Discriminator Loss: 1.0156... Generator Loss: 0.9553
Step : 1060 Epoch 1/1... Discriminator Loss: 1.4472... Generator Loss: 0.4920
Step : 1070 Epoch 1/1... Discriminator Loss: 1.7443... Generator Loss: 0.3170
Step : 1080 Epoch 1/1... Discriminator Loss: 1.1618... Generator Loss: 0.8864
Step : 1090 Epoch 1/1... Discriminator Loss: 1.5817... Generator Loss: 0.4448
Step : 1100 Epoch 1/1... Discriminator Loss: 1.2382... Generator Loss: 0.8794
Step : 1110 Epoch 1/1... Discriminator Loss: 1.3042... Generator Loss: 0.5525
Step : 1120 Epoch 1/1... Discriminator Loss: 1.0983... Generator Loss: 1.1821
Step : 1130 Epoch 1/1... Discriminator Loss: 1.2028... Generator Loss: 0.7458
Step : 1140 Epoch 1/1... Discriminator Loss: 1.5170... Generator Loss: 0.4518
Step : 1150 Epoch 1/1... Discriminator Loss: 1.1292... Generator Loss: 1.0817
Step : 1160 Epoch 1/1... Discriminator Loss: 1.9684... Generator Loss: 0.2888
Step : 1170 Epoch 1/1... Discriminator Loss: 1.1908... Generator Loss: 0.6911
Step : 1180 Epoch 1/1... Discriminator Loss: 1.1705... Generator Loss: 0.7824
Step : 1190 Epoch 1/1... Discriminator Loss: 1.7526... Generator Loss: 0.3033
Step : 1200 Epoch 1/1... Discriminator Loss: 0.7128... Generator Loss: 1.4505
Step : 1210 Epoch 1/1... Discriminator Loss: 1.1266... Generator Loss: 0.9658
Step : 1220 Epoch 1/1... Discriminator Loss: 1.0555... Generator Loss: 0.8325
Step : 1230 Epoch 1/1... Discriminator Loss: 1.4395... Generator Loss: 0.5714
Step : 1240 Epoch 1/1... Discriminator Loss: 1.4686... Generator Loss: 0.8211
Step : 1250 Epoch 1/1... Discriminator Loss: 1.4139... Generator Loss: 1.3205
Step : 1260 Epoch 1/1... Discriminator Loss: 1.0309... Generator Loss: 1.0540
Step : 1270 Epoch 1/1... Discriminator Loss: 1.3742... Generator Loss: 1.7748
Step : 1280 Epoch 1/1... Discriminator Loss: 1.9686... Generator Loss: 0.2523
Step : 1290 Epoch 1/1... Discriminator Loss: 1.1254... Generator Loss: 0.9172
Step : 1300 Epoch 1/1... Discriminator Loss: 1.0471... Generator Loss: 0.8271
Step : 1310 Epoch 1/1... Discriminator Loss: 0.7917... Generator Loss: 1.5449
Step : 1320 Epoch 1/1... Discriminator Loss: 1.1561... Generator Loss: 0.9800
Step : 1330 Epoch 1/1... Discriminator Loss: 1.0988... Generator Loss: 1.4353
Step : 1340 Epoch 1/1... Discriminator Loss: 1.2189... Generator Loss: 0.7626
Step : 1350 Epoch 1/1... Discriminator Loss: 1.3128... Generator Loss: 1.6655
Step : 1360 Epoch 1/1... Discriminator Loss: 0.9615... Generator Loss: 1.4741
Step : 1370 Epoch 1/1... Discriminator Loss: 1.0046... Generator Loss: 1.1577
Step : 1380 Epoch 1/1... Discriminator Loss: 1.6267... Generator Loss: 0.3727
Step : 1390 Epoch 1/1... Discriminator Loss: 1.3199... Generator Loss: 1.6366
Step : 1400 Epoch 1/1... Discriminator Loss: 1.2817... Generator Loss: 1.3999
Step : 1410 Epoch 1/1... Discriminator Loss: 1.1122... Generator Loss: 1.1034
Step : 1420 Epoch 1/1... Discriminator Loss: 1.3976... Generator Loss: 0.4858
Step : 1430 Epoch 1/1... Discriminator Loss: 0.9310... Generator Loss: 1.1484
Step : 1440 Epoch 1/1... Discriminator Loss: 1.2887... Generator Loss: 0.7013
Step : 1450 Epoch 1/1... Discriminator Loss: 1.4384... Generator Loss: 0.5414
Step : 1460 Epoch 1/1... Discriminator Loss: 1.1133... Generator Loss: 1.3919
Step : 1470 Epoch 1/1... Discriminator Loss: 0.6869... Generator Loss: 1.9900
Step : 1480 Epoch 1/1... Discriminator Loss: 1.3207... Generator Loss: 1.2728
Step : 1490 Epoch 1/1... Discriminator Loss: 0.8456... Generator Loss: 1.2420
Step : 1500 Epoch 1/1... Discriminator Loss: 1.5753... Generator Loss: 0.3760
Step : 1510 Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 1.0738
Step : 1520 Epoch 1/1... Discriminator Loss: 1.9155... Generator Loss: 0.2499
Step : 1530 Epoch 1/1... Discriminator Loss: 1.5738... Generator Loss: 0.3978
Step : 1540 Epoch 1/1... Discriminator Loss: 1.3590... Generator Loss: 0.9375
Step : 1550 Epoch 1/1... Discriminator Loss: 1.1834... Generator Loss: 0.9528
Step : 1560 Epoch 1/1... Discriminator Loss: 0.9834... Generator Loss: 1.0228
Step : 1570 Epoch 1/1... Discriminator Loss: 1.1380... Generator Loss: 1.0509
Step : 1580 Epoch 1/1... Discriminator Loss: 2.3452... Generator Loss: 0.5004
Step : 1590 Epoch 1/1... Discriminator Loss: 1.3429... Generator Loss: 0.8939
Step : 1600 Epoch 1/1... Discriminator Loss: 1.2122... Generator Loss: 1.0571
Step : 1610 Epoch 1/1... Discriminator Loss: 1.1937... Generator Loss: 1.1407
Step : 1620 Epoch 1/1... Discriminator Loss: 1.2086... Generator Loss: 0.7974
Step : 1630 Epoch 1/1... Discriminator Loss: 1.2369... Generator Loss: 0.9473
Step : 1640 Epoch 1/1... Discriminator Loss: 1.2748... Generator Loss: 0.7115
Step : 1650 Epoch 1/1... Discriminator Loss: 1.3715... Generator Loss: 0.6901
Step : 1660 Epoch 1/1... Discriminator Loss: 1.4610... Generator Loss: 0.5849
Step : 1670 Epoch 1/1... Discriminator Loss: 1.4770... Generator Loss: 0.5460
Step : 1680 Epoch 1/1... Discriminator Loss: 1.0270... Generator Loss: 1.5742
Step : 1690 Epoch 1/1... Discriminator Loss: 1.0732... Generator Loss: 0.7564
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Step : 7110 Epoch 1/1... Discriminator Loss: 1.2026... Generator Loss: 0.9054
Step : 7120 Epoch 1/1... Discriminator Loss: 1.4389... Generator Loss: 0.6815
Step : 7130 Epoch 1/1... Discriminator Loss: 1.4194... Generator Loss: 0.6523
Step : 7140 Epoch 1/1... Discriminator Loss: 1.5424... Generator Loss: 0.6448
Step : 7150 Epoch 1/1... Discriminator Loss: 1.4377... Generator Loss: 0.5692
Step : 7160 Epoch 1/1... Discriminator Loss: 1.3952... Generator Loss: 0.6677
Step : 7170 Epoch 1/1... Discriminator Loss: 1.2457... Generator Loss: 0.7765
Step : 7180 Epoch 1/1... Discriminator Loss: 1.2508... Generator Loss: 0.7170
Step : 7190 Epoch 1/1... Discriminator Loss: 1.4600... Generator Loss: 0.5605
Step : 7200 Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.5841
Step : 7210 Epoch 1/1... Discriminator Loss: 1.3923... Generator Loss: 0.6409
Step : 7220 Epoch 1/1... Discriminator Loss: 1.2451... Generator Loss: 0.7577
Step : 7230 Epoch 1/1... Discriminator Loss: 1.4133... Generator Loss: 0.6474
celeba complete

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.